All solid materials, including glass, have a property called elastic stiffness — also known as elastic modulus. It's a measure of how much force per unit area is needed to make the material bend or stretch. If that change is elastic, the material can totally recover its original shape and size once that force is stopped.

Elastic stiffness is critical for any materials in structural applications. Higher stiffness means that it can sustain the same force loading with a thinner material. For example, the structural glass in car windshields and in touch screens on smartphones can be made thinner and lighter if the glasses are stiffer. Glass fiber composites are widely used lightweight materials for cars, trucks, and wind turbines.

Lighter, stiffer glass can enable wind turbine blades to transfer wind power into electricity more efficiently because less wind power is “wasted” to make the blades rotate. It can also enable longer wind turbine blades, which can generate more electricity under the same wind speed.

Because glasses are amorphous — or disordered — materials, it is hard to predict their atomistic structures and the corresponding physical/chemical properties. Computer simulations are used to speed up the study of glasses but they require extensive computing time, making it impossible to investigate each possible glass composition.

Researchers used existing high-throughput computer simulations to generate data on the densities and elastic stiffnesses of various glasses. Then, they developed a machine learning model that is more suitable for a small amount of data. The model was designed so that it pays attention to the strength of the interaction between atoms. In essence, physics was used to give the model hints about what was important in the data, improving the quality of its predictions for new compositions.

While the machine learning model was trained with glasses made of silicon dioxide and one or two other additives, it could accurately predict the lightness and elastic stiffness of more complex glasses, with more than 10 different components. It can screen as many as 100,000 different compositions at once.

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